Displacement Prediction of Channel Slope Based on EEMD-IESSA-LSSVM Combined Algorithm

Slope displacement is a crucial factor that affects slope stability in engineering construction. The monitoring and prediction of slope displacement are especially important to ensure slope stability. To achieve this goal, it is necessary to establish an effective prediction model and analyze the patterns and trends of slope displacement. In recent years, monitoring efforts for high slopes have increased. With the growing availability of means and data for slope monitoring, the accurate prediction of slope displacement accidents has become even more critical. However, the lack of an accurate and efficient algorithm has resulted in an underutilization of available data. In this paper, we propose a combined EEMD-IESSA-LSSVM algorithm. Firstly, we use EEMD to decompose the slope displacement data and then introduce a more efficient and improved version of the sparrow search algorithm, called the irrational escape sparrow search algorithm (IESSA), by optimizing it and incorporating adaptive weight factors. We compare the IESSA algorithm with SSA, CSSOA, PSO, and GWO algorithms through validation using three different sets of benchmark functions. This comparison demonstrates that the IESSA algorithm achieves higher accuracy and a faster solving speed in solving these functions. Finally, we optimize LSSVM to predict slope displacement by incorporating rainfall and water level data. To verify the reliability of the algorithm, we conduct simulation analysis using slope data from the xtGTX1 monitoring point and the xtGTX3 monitoring point in the Yangtze River Xin Tan landslide and compare the results with those obtained using EEMD-LSSVM, EEMD-SSA-LSSVM, and EEMD-GWO-LSSVM. After numerical simulation, the goodness-of-fit of the two monitoring points is 0.98998 and 0.97714, respectively, which is 42% and 34% better than before. Using Friedman and Nemenyi tests, the algorithms were ranked as follows: IESSA-LSSVM > GWO-LSSVM > SSA-LSSVM > LSSVM. The findings indicate that the combined EEMD-IESSA-LSSVM algorithm exhibits a superior prediction ability and provides more accurate predictions for slope landslides compared to other algorithms.